Elena Zaslavsky

Understanding multicellular function and disease with human tissue-specific networks

Tissue and cell-type identity lie at the core of human physiology and disease. Understanding the genetic underpinnings of complex tissues and individual cell lineages is crucial for developing improved diagnostics and therapeutics. We present genome-wide functional interaction networks for 144 human tissues and cell types developed using a data-driven Bayesian methodology that integrates thousands of diverse experiments spanning tissue and disease states. Tissue-specific networks predict lineage-specific responses to perturbation, identify the changing functional roles of genes across tissues and illuminate relationships among diseases. We introduce NetWAS, which combines genes with nominally significant genome-wide association study (GWAS) P values and tissue-specific networks to identify disease-gene associations more accurately than GWAS alone. Our webserver, GIANT, provides an interface to human tissue networks through multi-gene queries, network visualization, analysis tools including NetWAS and downloadable networks. GIANT enables systematic exploration of the landscape of interacting genes that shape specialized cellular functions across more than a hundred human tissues and cell types.

Comparative analysis of methods for representing and searching for transcription factor binding sites

MOTIVATION An important step in unravelling the transcriptional regulatory network of an organism is to identify, for each transcription factor, all of its DNA binding sites. Several approaches are commonly used in searching for a transcription factors binding sites, including consensus sequences and position-specific scoring matrices. In addition, methods that compute the average number of nucleotide matches between a putative site and all known sites can be employed. Such basic approaches can all be naturally extended by incorporating pairwise nucleotide dependencies and per-position information content. In this paper, we evaluate the effectiveness of these basic approaches and their extensions in finding binding sites for a transcription factor of interest without erroneously identifying other genomic sequences. RESULTS In cross-validation testing on a dataset of Escherichia coli transcription factors and their binding sites, we show that there are statistically significant differences in how well various methods identify transcription factor binding sites. The use of per-position information content improves the performance of all basic approaches. Furthermore, including local pairwise nucleotide dependencies within binding site models results in statistically significant performance improvements for approaches based on nucleotide matches. Based on our analysis, the best results when searching for DNA binding sites of a particular transcription factor are obtained by methods that incorporate both information content and local pairwise correlations. AVAILABILITY The software is available at http://compbio.cs.princeton.edu/bindsites.

The Construction and Use of Log-Odds Substitution Scores for Multiple Sequence Alignment

Most pairwise and multiple sequence alignment programs seek alignments with optimal scores. Central to defining such scores is selecting a set of substitution scores for aligned amino acids or nucleotides. For local pairwise alignment, substitution scores are implicitly of log-odds form. We now extend the log-odds formalism to multiple alignments, using Bayesian methods to construct “BILD” (“Bayesian Integral Log-odds”) substitution scores from prior distributions describing columns of related letters. This approach has been used previously only to define scores for aligning individual sequences to sequence profiles, but it has much broader applicability. We describe how to calculate BILD scores efficiently, and illustrate their uses in Gibbs sampling optimization procedures, gapped alignment, and the construction of hidden Markov model profiles. BILD scores enable automated selection of optimal motif and domain model widths, and can inform the decision of whether to include a sequence in a multiple alignment, and the selection of insertion and deletion locations. Other applications include the classification of related sequences into subfamilies, and the definition of profile-profile alignment scores. Although a fully realized multiple alignment program must rely upon more than substitution scores, many existing multiple alignment programs can be modified to employ BILD scores. We illustrate how simple BILD score based strategies can enhance the recognition of DNA binding domains, including the Api-AP2 domain in Toxoplasma gondii and Plasmodium falciparum.

Antiviral response dictated by choreographed cascade of transcription factors.

The dendritic cell (DC) is a master regulator of immune responses. Pathogenic viruses subvert normal immune function in DCs through the expression of immune antagonists. Understanding how these antagonists interact with the host immune system requires knowledge of the underlying genetic regulatory network that operates during an uninhibited antiviral response. To isolate and identify this network, we studied DCs infected with Newcastle disease virus, which is able to stimulate innate immunity and DC maturation through activation of RIG-I signaling, but lacks the ability to evade the human IFN response. To analyze this experimental model, we developed a new approach integrating genome-wide expression kinetics and time-dependent promoter analysis. We found that the genetic program underlying the antiviral cell-state transition during the first 18 h postinfection could be explained by a single convergent regulatory network. Gene expression changes were driven by a stepwise multifactor cascading control mechanism, where the specific transcription factors controlling expression changed over time. Within this network, most individual genes were regulated by multiple factors, indicating robustness against virus-encoded immune evasion genes. In addition to effectively recapitulating current biological knowledge, we predicted, and validated experimentally, antiviral roles for several novel transcription factors. More generally, our results show how a genetic program can be temporally controlled through a single regulatory network to achieve the large-scale genetic reprogramming characteristic of cell-state transitions.

Astrocytic TYMP and VEGFA drive blood-brain barrier opening in inflammatory central nervous system lesions.

In inflammatory central nervous system conditions such as multiple sclerosis, breakdown of the blood-brain barrier is a key event in lesion pathogenesis, predisposing to oedema, excitotoxicity, and ingress of plasma proteins and inflammatory cells. Recently, we showed that reactive astrocytes drive blood-brain barrier opening, via production of vascular endothelial growth factor A (VEGFA). Here, we now identify thymidine phosphorylase (TYMP; previously known as endothelial cell growth factor 1, ECGF1) as a second key astrocyte-derived permeability factor, which interacts with VEGFA to induce blood-brain barrier disruption. The two are co-induced NFκB1-dependently in human astrocytes by the cytokine interleukin 1 beta (IL1B), and inactivation of Vegfa in vivo potentiates TYMP induction. In human central nervous system microvascular endothelial cells, VEGFA and the TYMP product 2-deoxy-d-ribose cooperatively repress tight junction proteins, driving permeability. Notably, this response represents part of a wider pattern of endothelial plasticity: 2-deoxy-d-ribose and VEGFA produce transcriptional programs encompassing angiogenic and permeability genes, and together regulate a third unique cohort. Functionally, each promotes proliferation and viability, and they cooperatively drive motility and angiogenesis. Importantly, introduction of either into mouse cortex promotes blood-brain barrier breakdown, and together they induce severe barrier disruption. In the multiple sclerosis model experimental autoimmune encephalitis, TYMP and VEGFA co-localize to reactive astrocytes, and correlate with blood-brain barrier permeability. Critically, blockade of either reduces neurologic deficit, blood-brain barrier disruption and pathology, and inhibiting both in combination enhances tissue preservation. Suggesting importance in human disease, TYMP and VEGFA both localize to reactive astrocytes in multiple sclerosis lesion samples. Collectively, these data identify TYMP as an astrocyte-derived permeability factor, and suggest TYMP and VEGFA together promote blood-brain barrier breakdown.

A combinatorial optimization approach for diverse motif finding applications.

BackgroundDiscovering approximately repeated patterns, or motifs, in biological sequences is an important and widely-studied problem in computational molecular biology. Most frequently, motif finding applications arise when identifying shared regulatory signals within DNA sequences or shared functional and structural elements within protein sequences. Due to the diversity of contexts in which motif finding is applied, several variations of the problem are commonly studied.ResultsWe introduce a versatile combinatorial optimization framework for motif finding that couples graph pruning techniques with a novel integer linear programming formulation. Our approach is flexible and robust enough to model several variants of the motif finding problem, including those incorporating substitution matrices and phylogenetic distances. Additionally, we give an approach for determining statistical significance of uncovered motifs. In testing on numerous DNA and protein datasets, we demonstrate that our approach typically identifies statistically significant motifs corresponding to either known motifs or other motifs of high conservation. Moreover, in most cases, our approach finds provably optimal solutions to the underlying optimization problem.ConclusionOur results demonstrate that a combined graph theoretic and mathematical programming approach can be the basis for effective and powerful techniques for diverse motif finding applications.

M are better than one

MOTIVATION Identifying regulatory elements in genomic sequences is a key component in understanding the control of gene expression. Computationally, this problem is often addressed by motif discovery, where the goal is to find a set of mutually similar subsequences within a collection of input sequences. Though motif discovery is widely studied and many approaches to it have been suggested, it remains a challenging and as yet unresolved problem. RESULTS We introduce SAMF (Solution-Aggregating Motif Finder), a novel approach for motif discovery. SAMF is based on a Markov Random Field formulation, and its key idea is to uncover and aggregate multiple statistically significant solutions to the given motif finding problem. In contrast to many earlier methods, SAMF does not require prior estimates on the number of motif instances present in the data, is not limited by motif length, and allows motifs to overlap. Though SAMF is broadly applicable, these features make it particularly well suited for addressing the challenges of prokaryotic regulatory element detection. We test SAMFs ability to find transcription factor binding sites in an Escherichia coli dataset and show that it outperforms previous methods. Additionally, we uncover a number of previously unidentified binding sites in this data, and provide evidence that they correspond to actual regulatory elements. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

CellCODE: A robust latent variable approach to differential expression analysis for heterogeneous cell populations

MOTIVATION Identifying alterations in gene expression associated with different clinical states is important for the study of human biology. However, clinical samples used in gene expression studies are often derived from heterogeneous mixtures with variable cell-type composition, complicating statistical analysis. Considerable effort has been devoted to modeling sample heterogeneity, and presently, there are many methods that can estimate cell proportions or pure cell-type expression from mixture data. However, there is no method that comprehensively addresses mixture analysis in the context of differential expression without relying on additional proportion information, which can be inaccurate and is frequently unavailable. RESULTS In this study, we consider a clinically relevant situation where neither accurate proportion estimates nor pure cell expression is of direct interest, but where we are rather interested in detecting and interpreting relevant differential expression in mixture samples. We develop a method, Cell-type COmputational Differential Estimation (CellCODE), that addresses the specific statistical question directly, without requiring a physical model for mixture components. Our approach is based on latent variable analysis and is computationally transparent; it requires no additional experimental data, yet outperforms existing methods that use independent proportion measurements. CellCODE has few parameters that are robust and easy to interpret. The method can be used to track changes in proportion, improve power to detect differential expression and assign the differentially expressed genes to the correct cell type.

Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases

Many functionally important interactions between genes and proteins involved in immunological diseases and processes are unknown. The exponential growth in public high-throughput data offers an opportunity to expand this knowledge. To unlock human-immunology-relevant insight contained in the global biomedical research effort, including all public high-throughput datasets, we performed immunological-pathway-focused Bayesian integration of a comprehensive, heterogeneous compendium comprising 38,088 genome-scale experiments. The distillation of this knowledge into immunological networks of functional relationships between molecular entities (ImmuNet), and tools to mine this resource, are accessible to the public at http://immunet.princeton.edu. The predictive capacity of ImmuNet, established by rigorous statistical validation, is easily accessed by experimentalists to generate data-driven hypotheses. We demonstrate the power of this approach through the identification of unique host-virus interaction responses, and we show how ImmuNet complements genetic studies by predicting disease-associated genes. ImmuNet should be widely beneficial for investigating the mechanisms of the human immune system and immunological diseases.

Human Dendritic Cell Response Signatures Distinguish 1918, Pandemic, and Seasonal H1N1 Influenza Viruses.

ABSTRACT Influenza viruses continue to present global threats to human health. Antigenic drift and shift, genetic reassortment, and cross-species transmission generate new strains with differences in epidemiology and clinical severity. We compared the temporal transcriptional responses of human dendritic cells (DC) to infection with two pandemic (A/Brevig Mission/1/1918, A/California/4/2009) and two seasonal (A/New Caledonia/20/1999, A/Texas/36/1991) H1N1 influenza viruses. Strain-specific response differences included stronger activation of NF-κB following infection with A/New Caledonia/20/1999 and a unique cluster of genes expressed following infection with A/Brevig Mission/1/1918. A common antiviral program showing strain-specific timing was identified in the early DC response and found to correspond with reported transcript changes in blood during symptomatic human influenza virus infection. Comparison of the global responses to the seasonal and pandemic strains showed that a dramatic divergence occurred after 4 h, with only the seasonal strains inducing widespread mRNA loss. IMPORTANCE Continuously evolving influenza viruses present a global threat to human health; however, these host responses display strain-dependent differences that are incompletely understood. Thus, we conducted a detailed comparative study assessing the immune responses of human DC to infection with two pandemic and two seasonal H1N1 influenza strains. We identified in the immune response to viral infection both common and strain-specific features. Among the stain-specific elements were a time shift of the interferon-stimulated gene response, selective induction of NF-κB signaling by one of the seasonal strains, and massive RNA degradation as early as 4 h postinfection by the seasonal, but not the pandemic, viruses. These findings illuminate new aspects of the distinct differences in the immune responses to pandemic and seasonal influenza viruses.